Abstract: Children with fetal alcohol spectrum disorder (FASD) may exhibit physical, cognitive, behavioral, and learning disabilities caused by maternal consumption of alcohol during pregnancy. FASD has a high rate of co-morbidity with attention deficit hyperactivity disorder and accurate diagnosis can be both challenging and time consuming. Current diagnostic approaches specify a multidisciplinary team including physicians, psychologists, dysmorphologists, and occupational therapists to diagnose each child from up to 2 full days of assessment. Here we have used machine learning techniques to test the hypothesis that an objective assessment of FASD can be obtained based on automated classification of data from 3 sources, obtained for both children with FASD (patients) and age-matched controls: neuroimaging, neuropsychological tests, and natural eye movement behavior. Diffusion tensor imaging (DTI) provided 48 features from 6 parts of the corpus callosum: genu, rostral, anterior, posterior, isthmus and splenium. Components of the NEPSY-II battery of neuropsychological tests provided 20 features that quantify executive functions, memory, attention and response inhibition. Finally, natural eye movement behavior was recorded while subjects watched a 5-minute video; the natural viewing (NV) feature was extracted through a 3-layer convolutional deep neural network and a 2-layer classifier. Using support vector machine-recursive feature elimination (SVM-RFE) and cross-validation, we report classification accuracy from the obtained objective profile. The DTI features alone on 35 controls and 42 patients yielded 81.6% correct; the NEPSY-II features alone on 71 controls and 58 patients yielded 89.1% correct, and the NV feature on 53 controls and 47 patients yielded 76% correct. When combining features from different sources, starting with all available features for the 24 controls and 23 patients who completed all three series of tests, 2 NEPSY-II features, 2 DTI features and the NV feature were selected to achieve 97.9% correct. Moreover, a classifier trained only with the selected NV and 2 NEPSY-II features on the same group reached a classification accuracy of 91.5%, which provides a relatively high-throughput and low-cost procedure for assessment. In conclusion, we found that a mix of NEPSY-II, DTI, and NV features works best, but the NEPSY-II+NV alternative also performs well. Our feature discovery and elimination reduced the original data to just a few focused measures, eliminating the less predictive tests. The discovered features may provide insight into which brain and cognitive functions are most affected by prenatal alcohol exposure.